Immigration status and property crime:an application of estimators for underreported outcomes

This paper studies the individual-level relationship between immigration and property crime in England and Wales using crime self-reports from the Crime and Justice Survey. Models that account for underreporting are used, since this is a major concern in crime self-reports. The results indicate that the reported crime is substantially underreported, but if anything, immigrants are less likely to underreport than natives. More importantly, controlling for underreporting and basic demographics, the estimates across all model specifications, although imprecise, indicate that immigration status and property crime are negatively associated. We finally find that the estimated relationship between immigration status and property crime differs across regions and ethnic groups

Immigration and Crime: A Microeconometric Study

Although the relationship between immigration and crime has been a very controversial subject in the UK, the empirical evidence is limited. This thesis intends to narrow this gap by providing a comprehensive investigation for England and Wales of immigrants’ both active and passive involvement in criminal activities. Before exploring the aforementioned relationship, Chapter 1 discusses and provides solutions to an identification issue that afflicts leading models for under-reported count data. It also provides some tips for practitioners who intend to use these models in applied research. These findings are important for this thesis, since estimators that deal with under-reporting are considered in Chapter 2. Chapter 2 studies the individual-level relationship between immigration and crime using self-reported crime data. Although this work focuses on property crime, violent crime is also considered. Both binary and count data models that account for under-reporting are used, since under-reporting is a concern in crime self-reports. Our findings suggest that, if anything, immigrants under-report by less than natives. Most importantly, these models predict that after controlling for under-reporting and basic demographics, immigrants are less involved in criminal activities, but the estimated difference is statistically insignificant. Nevertheless, an extensive sensitivity analysis indicates that this estimate is very robust, suggesting that this relationship exists, but data limitations and complexities of the considered models reduce the precision of the estimated coefficient. Finally, Chapter 3 comprehensively examines whether victimization experiences are different between immigrants and natives. Very interestingly, although observed demographic differences can explain the positive property crime victimization-immigration differentials, unobserved factors give rise to a negative association between immigration and violent victimization. All results suggest that this is due to immigrants’ lifestyle choices associated with lower victimization risks. As will be explained throughout Chapter 3, this finding is consistent with the findings of Chapter 2

Identification issues in models for underreported counts

In this note we study the conditions under which leading models for underreported counts are identified. In particular, we highlight a peculiar identification problem that afflicts two of the most popular models in this class.

Deep Affordance-grounded Sensorimotor Object Recognition

It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them. This fact has recently motivated the "sensorimotor" approach to the challenging task of automatic object recognition, where both information sources are fused to improve robustness. In this work, the aforementioned paradigm is adopted, surpassing current limitations of sensorimotor object recognition research. Specifically, the deep learning paradigm is introduced to the problem for the first time, developing a number of novel neuro-biologically and neuro-physiologically inspired architectures that utilize state-of-the-art neural networks for fusing the available information sources in multiple ways. The proposed methods are evaluated using a large RGB-D corpus, which is specifically collected for the task of sensorimotor object recognition and is made publicly available. Experimental results demonstrate the utility of affordance information to object recognition, achieving an up to 29% relative error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201